Feature Engineering and Creation

Imports and Setup

import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
from collections import Counter
from sklearn.decomposition import TruncatedSVD, PCA
from sklearn.feature_selection import SelectKBest

from sklearn.preprocessing import StandardScaler, OrdinalEncoder

sns.set(style='darkgrid', palette='pastel')
pd.options.display.max_columns = None

data = pd.read_csv('../data/processed/0_cleaned.csv')
data_backup = data.copy()

label = data[['Value', 'Wage']]
data.drop(columns=['Value', 'Wage'], inplace=True)

data.head(5)

	Age	Nationality	Overall	Potential	Club	Special	Preferred Foot	International Reputation	Weak Foot	Skill Moves	Work Rate	Body Type	Position	Jersey Number	Height	Weight	LS	ST	RS	LW	LF	CF	RF	RW	LAM	CAM	RAM	LM	LCM	CM	RCM	RM	LWB	LDM	CDM	RDM	RWB	LB	LCB	CB	RCB	RB	Crossing	Finishing	HeadingAccuracy	ShortPassing	Volleys	Dribbling	Curve	FKAccuracy	LongPassing	BallControl	Acceleration	SprintSpeed	Agility	Reactions	Balance	ShotPower	Jumping	Stamina	Strength	LongShots	Aggression	Interceptions	Positioning	Vision	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes
0	31.0	Argentina	94.0	94.0	FC Barcelona	2202.0	Left	5.0	4.0	4.0	Medium/ Medium	Messi	RF	10.0	170.18	159.0	88.0	88.0	88.0	92.0	93.0	93.0	93.0	92.0	93.0	93.0	93.0	91.0	84.0	84.0	84.0	91.0	64.0	61.0	61.0	61.0	64.0	59.0	47.0	47.0	47.0	59.0	84.0	95.0	70.0	90.0	86.0	97.0	93.0	94.0	87.0	96.0	91.0	86.0	91.0	95.0	95.0	85.0	68.0	72.0	59.0	94.0	48.0	22.0	94.0	94.0	75.0	96.0	33.0	28.0	26.0	6.0	11.0	15.0	14.0	8.0
1	33.0	Portugal	94.0	94.0	Juventus	2228.0	Right	5.0	4.0	5.0	High/ Low	C. Ronaldo	ST	7.0	187.96	183.0	91.0	91.0	91.0	89.0	90.0	90.0	90.0	89.0	88.0	88.0	88.0	88.0	81.0	81.0	81.0	88.0	65.0	61.0	61.0	61.0	65.0	61.0	53.0	53.0	53.0	61.0	84.0	94.0	89.0	81.0	87.0	88.0	81.0	76.0	77.0	94.0	89.0	91.0	87.0	96.0	70.0	95.0	95.0	88.0	79.0	93.0	63.0	29.0	95.0	82.0	85.0	95.0	28.0	31.0	23.0	7.0	11.0	15.0	14.0	11.0
2	26.0	Brazil	92.0	93.0	Paris Saint-Germain	2143.0	Right	5.0	5.0	5.0	High/ Medium	Neymar	LW	10.0	175.26	150.0	84.0	84.0	84.0	89.0	89.0	89.0	89.0	89.0	89.0	89.0	89.0	88.0	81.0	81.0	81.0	88.0	65.0	60.0	60.0	60.0	65.0	60.0	47.0	47.0	47.0	60.0	79.0	87.0	62.0	84.0	84.0	96.0	88.0	87.0	78.0	95.0	94.0	90.0	96.0	94.0	84.0	80.0	61.0	81.0	49.0	82.0	56.0	36.0	89.0	87.0	81.0	94.0	27.0	24.0	33.0	9.0	9.0	15.0	15.0	11.0
3	27.0	Spain	91.0	93.0	Manchester United	1471.0	Right	4.0	3.0	1.0	Medium/ Medium	Lean	GK	1.0	193.04	168.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	17.0	13.0	21.0	50.0	13.0	18.0	21.0	19.0	51.0	42.0	57.0	58.0	60.0	90.0	43.0	31.0	67.0	43.0	64.0	12.0	38.0	30.0	12.0	68.0	40.0	68.0	15.0	21.0	13.0	90.0	85.0	87.0	88.0	94.0
4	27.0	Belgium	91.0	92.0	Manchester City	2281.0	Right	4.0	5.0	4.0	High/ High	Normal	RCM	7.0	180.34	154.0	82.0	82.0	82.0	87.0	87.0	87.0	87.0	87.0	88.0	88.0	88.0	88.0	87.0	87.0	87.0	88.0	77.0	77.0	77.0	77.0	77.0	73.0	66.0	66.0	66.0	73.0	93.0	82.0	55.0	92.0	82.0	86.0	85.0	83.0	91.0	91.0	78.0	76.0	79.0	91.0	77.0	91.0	63.0	90.0	75.0	91.0	76.0	61.0	87.0	94.0	79.0	88.0	68.0	58.0	51.0	15.0	13.0	5.0	10.0	13.0

label.isnull().sum()

Value    0
Wage     0
dtype: int64

Transform Categorical Features

categorical_columns = np.asarray([not np.issubdtype(data[col].dtype, np.number) for col in data.columns], dtype=np.bool)
cat_col_names = [col for col in data.columns if not np.issubdtype(data[col].dtype, np.number)]
num_col_names = [col for col in data.columns if np.issubdtype(data[col].dtype, np.number)]

categorical_data = data.iloc[:, categorical_columns]
non_categorical = data.iloc[:, ~categorical_columns]
categorical_data.reset_index(inplace=True, drop=True)
non_categorical.reset_index(inplace=True, drop=True)

Transform Using OneHotEncoding

cardinality_threshold = 10
cols_to_reduce_dim = []
for c in cat_col_names:
    levels = categorical_data[c].drop_duplicates().shape[0]
    if levels > cardinality_threshold:
        cols_to_reduce_dim.append(c)      

df_reduce = categorical_data[cols_to_reduce_dim]
df_reduce = pd.get_dummies(df_reduce)

svd = PCA(n_components=0.85)
df_reduce = svd.fit_transform(df_reduce)

column_names = []
for col in range(df_reduce.shape[1]):
    column_names.append('reduced_col_{}'.format(col))
df_reduce = pd.DataFrame(df_reduce, columns=column_names)

df_dummies = categorical_data.drop(cols_to_reduce_dim, axis=1)
df_dummies = pd.get_dummies(df_dummies)

df_dummies = df_dummies.join(df_reduce)
df_dummies = df_dummies.join(non_categorical)

Transform Using Ordinal Encoding

encoder = OrdinalEncoder()
encoder.fit(categorical_data)
column_names = categorical_data.columns
categorical_data = encoder.transform(categorical_data)
df_ordinal = pd.DataFrame(categorical_data, columns=column_names)
del categorical_data
df_ordinal = non_categorical.join(df_ordinal)
del non_categorical

Normalize Data

Normalize Ordinal Encoded Data

scaler = StandardScaler()
column_names = df_ordinal.columns
df_ordinal = scaler.fit_transform(df_ordinal)
df_ordinal = pd.DataFrame(df_ordinal, columns=column_names)
df_ordinal.head()
del column_names

Normalize One Hot Encoded Data

scaler = StandardScaler()
column_names = df_dummies.columns
df_dummies = scaler.fit_transform(df_dummies)
df_dummies = pd.DataFrame(df_dummies, columns=column_names)
df_dummies.head()
del column_names

df_dummies.head()

	Preferred Foot_Left	Preferred Foot_Right	Work Rate_High/ High	Work Rate_High/ Low	Work Rate_High/ Medium	Work Rate_Low/ High	Work Rate_Low/ Low	Work Rate_Low/ Medium	Work Rate_Medium/ High	Work Rate_Medium/ Low	Work Rate_Medium/ Medium	Body Type_Akinfenwa	Body Type_C. Ronaldo	Body Type_Courtois	Body Type_Lean	Body Type_Messi	Body Type_Neymar	Body Type_Normal	Body Type_PLAYER_BODY_TYPE_25	Body Type_Shaqiri	Body Type_Stocky	reduced_col_0	reduced_col_1	reduced_col_2	reduced_col_3	reduced_col_4	reduced_col_5	reduced_col_6	reduced_col_7	reduced_col_8	reduced_col_9	reduced_col_10	reduced_col_11	reduced_col_12	reduced_col_13	reduced_col_14	reduced_col_15	reduced_col_16	reduced_col_17	reduced_col_18	reduced_col_19	reduced_col_20	reduced_col_21	reduced_col_22	reduced_col_23	reduced_col_24	reduced_col_25	reduced_col_26	reduced_col_27	reduced_col_28	reduced_col_29	reduced_col_30	reduced_col_31	reduced_col_32	reduced_col_33	reduced_col_34	reduced_col_35	reduced_col_36	reduced_col_37	reduced_col_38	reduced_col_39	reduced_col_40	reduced_col_41	reduced_col_42	reduced_col_43	reduced_col_44	reduced_col_45	reduced_col_46	reduced_col_47	reduced_col_48	reduced_col_49	reduced_col_50	reduced_col_51	reduced_col_52	reduced_col_53	reduced_col_54	reduced_col_55	reduced_col_56	reduced_col_57	reduced_col_58	reduced_col_59	reduced_col_60	reduced_col_61	reduced_col_62	reduced_col_63	reduced_col_64	reduced_col_65	reduced_col_66	reduced_col_67	reduced_col_68	reduced_col_69	reduced_col_70	reduced_col_71	reduced_col_72	reduced_col_73	reduced_col_74	reduced_col_75	reduced_col_76	reduced_col_77	reduced_col_78	reduced_col_79	reduced_col_80	reduced_col_81	reduced_col_82	reduced_col_83	reduced_col_84	reduced_col_85	reduced_col_86	reduced_col_87	reduced_col_88	reduced_col_89	reduced_col_90	reduced_col_91	reduced_col_92	reduced_col_93	reduced_col_94	reduced_col_95	reduced_col_96	reduced_col_97	reduced_col_98	reduced_col_99	reduced_col_100	reduced_col_101	reduced_col_102	reduced_col_103	reduced_col_104	reduced_col_105	reduced_col_106	reduced_col_107	reduced_col_108	reduced_col_109	reduced_col_110	reduced_col_111	reduced_col_112	reduced_col_113	reduced_col_114	reduced_col_115	reduced_col_116	reduced_col_117	reduced_col_118	reduced_col_119	reduced_col_120	reduced_col_121	reduced_col_122	reduced_col_123	reduced_col_124	reduced_col_125	reduced_col_126	reduced_col_127	reduced_col_128	reduced_col_129	reduced_col_130	reduced_col_131	reduced_col_132	reduced_col_133	reduced_col_134	reduced_col_135	reduced_col_136	reduced_col_137	reduced_col_138	reduced_col_139	reduced_col_140	reduced_col_141	reduced_col_142	reduced_col_143	reduced_col_144	reduced_col_145	reduced_col_146	reduced_col_147	reduced_col_148	reduced_col_149	reduced_col_150	reduced_col_151	reduced_col_152	reduced_col_153	reduced_col_154	reduced_col_155	reduced_col_156	reduced_col_157	reduced_col_158	reduced_col_159	reduced_col_160	reduced_col_161	reduced_col_162	reduced_col_163	reduced_col_164	reduced_col_165	reduced_col_166	reduced_col_167	reduced_col_168	reduced_col_169	reduced_col_170	reduced_col_171	reduced_col_172	reduced_col_173	reduced_col_174	reduced_col_175	reduced_col_176	reduced_col_177	reduced_col_178	reduced_col_179	reduced_col_180	reduced_col_181	reduced_col_182	reduced_col_183	reduced_col_184	reduced_col_185	reduced_col_186	reduced_col_187	reduced_col_188	reduced_col_189	reduced_col_190	reduced_col_191	reduced_col_192	reduced_col_193	reduced_col_194	reduced_col_195	reduced_col_196	reduced_col_197	reduced_col_198	reduced_col_199	reduced_col_200	reduced_col_201	reduced_col_202	reduced_col_203	reduced_col_204	reduced_col_205	reduced_col_206	reduced_col_207	reduced_col_208	reduced_col_209	reduced_col_210	reduced_col_211	reduced_col_212	reduced_col_213	reduced_col_214	reduced_col_215	reduced_col_216	reduced_col_217	reduced_col_218	reduced_col_219	reduced_col_220	reduced_col_221	reduced_col_222	reduced_col_223	reduced_col_224	reduced_col_225	reduced_col_226	reduced_col_227	reduced_col_228	reduced_col_229	reduced_col_230	reduced_col_231	reduced_col_232	reduced_col_233	reduced_col_234	reduced_col_235	reduced_col_236	reduced_col_237	reduced_col_238	reduced_col_239	reduced_col_240	reduced_col_241	reduced_col_242	reduced_col_243	reduced_col_244	reduced_col_245	reduced_col_246	reduced_col_247	reduced_col_248	reduced_col_249	reduced_col_250	reduced_col_251	reduced_col_252	reduced_col_253	reduced_col_254	reduced_col_255	reduced_col_256	reduced_col_257	reduced_col_258	reduced_col_259	reduced_col_260	reduced_col_261	reduced_col_262	reduced_col_263	reduced_col_264	reduced_col_265	reduced_col_266	reduced_col_267	reduced_col_268	reduced_col_269	reduced_col_270	reduced_col_271	reduced_col_272	reduced_col_273	reduced_col_274	reduced_col_275	reduced_col_276	reduced_col_277	reduced_col_278	reduced_col_279	reduced_col_280	reduced_col_281	reduced_col_282	reduced_col_283	reduced_col_284	reduced_col_285	reduced_col_286	reduced_col_287	reduced_col_288	reduced_col_289	reduced_col_290	reduced_col_291	reduced_col_292	reduced_col_293	reduced_col_294	reduced_col_295	reduced_col_296	reduced_col_297	reduced_col_298	reduced_col_299	reduced_col_300	reduced_col_301	reduced_col_302	reduced_col_303	reduced_col_304	reduced_col_305	reduced_col_306	reduced_col_307	reduced_col_308	reduced_col_309	reduced_col_310	reduced_col_311	reduced_col_312	reduced_col_313	reduced_col_314	reduced_col_315	reduced_col_316	reduced_col_317	reduced_col_318	reduced_col_319	reduced_col_320	reduced_col_321	reduced_col_322	reduced_col_323	reduced_col_324	reduced_col_325	reduced_col_326	reduced_col_327	reduced_col_328	reduced_col_329	reduced_col_330	reduced_col_331	reduced_col_332	reduced_col_333	reduced_col_334	reduced_col_335	reduced_col_336	reduced_col_337	reduced_col_338	reduced_col_339	reduced_col_340	reduced_col_341	reduced_col_342	reduced_col_343	reduced_col_344	reduced_col_345	reduced_col_346	reduced_col_347	reduced_col_348	reduced_col_349	reduced_col_350	reduced_col_351	reduced_col_352	reduced_col_353	reduced_col_354	reduced_col_355	reduced_col_356	reduced_col_357	reduced_col_358	reduced_col_359	reduced_col_360	reduced_col_361	reduced_col_362	reduced_col_363	reduced_col_364	reduced_col_365	reduced_col_366	reduced_col_367	reduced_col_368	reduced_col_369	reduced_col_370	reduced_col_371	reduced_col_372	reduced_col_373	reduced_col_374	reduced_col_375	reduced_col_376	reduced_col_377	reduced_col_378	reduced_col_379	reduced_col_380	reduced_col_381	reduced_col_382	reduced_col_383	reduced_col_384	reduced_col_385	reduced_col_386	reduced_col_387	reduced_col_388	reduced_col_389	reduced_col_390	reduced_col_391	reduced_col_392	reduced_col_393	reduced_col_394	reduced_col_395	reduced_col_396	reduced_col_397	reduced_col_398	reduced_col_399	reduced_col_400	reduced_col_401	reduced_col_402	reduced_col_403	reduced_col_404	reduced_col_405	reduced_col_406	reduced_col_407	reduced_col_408	reduced_col_409	reduced_col_410	reduced_col_411	reduced_col_412	reduced_col_413	reduced_col_414	reduced_col_415	reduced_col_416	reduced_col_417	reduced_col_418	reduced_col_419	reduced_col_420	reduced_col_421	reduced_col_422	reduced_col_423	reduced_col_424	reduced_col_425	reduced_col_426	reduced_col_427	reduced_col_428	reduced_col_429	reduced_col_430	reduced_col_431	reduced_col_432	reduced_col_433	reduced_col_434	reduced_col_435	reduced_col_436	Age	Overall	Potential	Special	International Reputation	Weak Foot	Skill Moves	Jersey Number	Height	Weight	LS	ST	RS	LW	LF	CF	RF	RW	LAM	CAM	RAM	LM	LCM	CM	RCM	RM	LWB	LDM	CDM	RDM	RWB	LB	LCB	CB	RCB	RB	Crossing	Finishing	HeadingAccuracy	ShortPassing	Volleys	Dribbling	Curve	FKAccuracy	LongPassing	BallControl	Acceleration	SprintSpeed	Agility	Reactions	Balance	ShotPower	Jumping	Stamina	Strength	LongShots	Aggression	Interceptions	Positioning	Vision	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes
0	1.819966	-1.819966	-0.243320	-0.200086	-0.460142	-0.157398	-0.043311	-0.159226	-0.320339	-0.221602	0.922535	-0.007421	-0.007421	-0.007421	-0.739256	134.751623	-0.007421	-1.183518	-0.007421	-0.007421	-0.258813	-0.025030	-0.124137	0.043661	-0.489503	0.076626	-0.052710	0.045809	-0.696539	0.254391	-1.116797	-1.076762	0.449501	3.152894	1.795082	0.980016	0.278538	0.393420	0.053375	0.385782	0.127697	0.457118	0.091012	-0.003341	0.100609	-0.026914	-0.062550	-0.038607	0.021641	0.029073	-0.010898	-0.012549	-0.010883	-0.034975	-0.153374	-0.018113	-0.056407	0.063416	0.007848	-0.077980	-0.149507	0.387160	-0.204118	0.021048	0.030567	-0.068259	0.290932	0.124257	-0.175111	0.068023	-0.000238	-0.026643	-0.130464	-0.113250	-0.065391	-0.025833	0.042499	-0.159169	0.293210	0.920934	0.159750	0.043026	-0.080303	0.067361	-0.138927	0.303237	0.429450	0.316550	-0.172149	-0.153590	-0.046946	-0.050547	-0.049805	-0.029740	-0.001275	-0.007572	-0.010982	-0.173994	0.016092	-0.126822	-0.189650	-0.079487	-0.120204	-0.180438	0.253743	-0.493973	0.735913	0.004201	1.103071	-0.022659	-0.204418	-3.121383	-2.655840	12.075566	-11.710633	-0.778650	-12.673207	2.489642	1.884204	0.715373	-4.532138	0.480582	1.045607	2.115204	-3.982130	2.712623	-1.487675	-1.367518	0.599605	-6.291128	0.028271	2.856445	-4.943365	3.262012	0.698759	0.452892	3.169241	0.733207	0.189608	0.014154	-0.535051	-0.108434	0.201775	0.370942	0.469042	0.740614	0.502943	0.955104	0.536674	0.063468	-0.200761	0.358547	0.392161	0.301760	-0.205254	1.360603	-0.295316	0.139560	0.245110	1.115577	0.721629	-0.205635	0.452851	0.520400	1.370042	0.514755	-0.662536	0.561032	-0.023284	0.150546	-0.121182	-0.042828	-0.133688	0.062027	0.014256	0.026487	-0.075329	-0.064654	0.017431	0.042805	0.022480	0.038812	0.030842	-0.006894	-0.015514	0.013093	-0.015921	-0.004244	-0.039522	-0.031782	-0.005264	0.017619	-0.011958	0.030580	0.003526	0.034625	-0.018188	0.001857	0.049740	-0.022147	0.025406	0.002060	0.010423	-0.035769	0.066609	0.062303	0.048421	0.005261	0.006434	-0.001493	0.018563	-0.005977	-0.034769	0.078932	0.042463	-0.069521	0.008345	0.070999	0.064913	0.125622	-0.167970	0.029444	0.049896	-0.027177	-0.079337	-0.037055	0.104196	0.063288	0.099125	-0.213047	-0.108603	-0.012141	-0.155789	-0.137741	-0.113855	0.055748	0.264588	-0.138650	-0.230307	0.025343	-0.014355	0.371855	-0.226583	0.022212	-0.218888	0.097323	-0.539793	-0.186057	0.174866	0.040428	0.411391	-0.193008	-0.312114	0.529564	-0.795016	0.107424	0.376049	0.333085	0.033996	-0.077841	0.056613	-0.071118	0.517264	-0.178085	-0.084891	-0.164599	-0.380301	-0.363339	-0.341733	0.480933	0.255668	-0.144997	0.058395	0.147948	-0.047594	-0.273265	-0.139852	-0.016671	-0.080807	0.155682	-0.267176	0.101779	-0.001658	-0.240250	-0.050075	0.098974	0.064657	-0.005191	-0.210237	0.210051	0.143640	-0.110922	0.127793	-0.228330	-0.250901	-0.115086	-0.031032	0.456749	0.273699	-0.183640	1.058342	0.367576	0.533280	-0.588887	-0.099736	0.258391	-0.694769	-0.584398	0.553087	0.170711	0.132539	-0.534545	-0.458022	0.345254	-0.115451	0.096934	-0.377557	-0.190382	0.270146	-0.024930	0.151907	-0.139731	0.000784	-0.011357	-0.045590	0.014198	0.003449	-0.006683	0.098726	-0.025701	-0.021375	0.007932	-0.007125	-0.007166	0.032126	0.034834	-0.016020	-0.006656	-0.009631	-0.005452	0.101521	-0.001392	-0.035765	-0.040548	-0.012966	-0.026198	0.016646	0.015037	0.018060	0.005419	-0.000264	-0.008489	0.002495	-0.009407	0.009845	-0.008405	-0.005385	0.001375	-0.000913	-0.000753	0.000207	0.000363	-1.363825e-13	-1.448941e-13	-7.334690e-14	-2.202886e-13	-1.315246e-13	3.045389e-14	5.116986e-14	-1.132121e-13	6.418524e-15	2.731428e-14	-2.437712e-14	2.464209e-14	-1.299866e-13	-1.767396e-13	6.222596e-15	-8.049848e-14	1.444140e-13	-9.275325e-14	-1.132351e-13	-1.626771e-13	5.938240e-14	-2.083328e-13	1.282210e-13	1.428713e-13	-1.582860e-13	-6.093101e-13	-1.462930e-13	-1.788843e-13	5.282681e-14	-0.000133	0.000393	-0.000119	0.000785	0.000306	-0.000736	0.004750	-0.005332	-0.002994	0.009795	0.001406	-0.004008	-0.011407	0.033392	-0.036956	0.040971	-0.019653	-0.022804	0.052971	0.038464	0.054940	0.023719	0.058886	-0.014836	-0.045852	-0.057994	-0.051461	0.015118	0.029555	-0.075096	-0.007399	0.086235	-0.020287	-0.163265	-0.057984	-0.181406	-0.097463	-0.130997	0.133144	0.049848	0.067051	0.053665	-0.134891	-0.022085	0.310444	-0.202646	0.064657	0.078541	0.156599	-0.157880	0.059175	0.136121	0.081225	0.126736	-0.045943	0.263086	0.313761	-0.161997	-0.257737	0.750700	-0.087125	-0.008060	0.315903	0.290849	-0.078487	0.158924	-0.068475	1.258441	4.013364	3.697415	2.213984	9.864420	1.593944	2.167171	-0.597693	-1.646010	-0.447583	1.818665	1.818665	1.818665	1.901392	1.973099	1.973099	1.973099	1.901392	1.957564	1.957564	1.957564	1.828702	1.603458	1.603458	1.603458	1.828702	0.646400	0.519988	0.519988	0.519988	0.646400	0.427328	-0.114632	-0.114632	-0.114632	0.427328	1.865922	2.532567	1.018552	2.130287	2.435355	2.201445	2.491426	2.925736	2.237037	2.255198	1.767621	1.452129	1.862187	3.680643	2.195382	1.713704	0.246247	0.552403	-0.502679	2.434582	-0.453087	-1.193365	2.255244	2.869906	1.684414	3.266205	-0.717531	-0.909268	-0.923569	-0.599961	-0.318908	-0.074659	-0.140241	-0.485161
1	-0.549461	0.549461	-0.243320	4.997854	-0.460142	-0.157398	-0.043311	-0.159226	-0.320339	-0.221602	-1.083970	-0.007421	134.751623	-0.007421	-0.739256	-0.007421	-0.007421	-1.183518	-0.007421	-0.007421	-0.258813	2.398178	1.008121	0.682379	-0.138231	0.163670	-0.075588	-0.201962	0.072392	-0.071780	0.036250	-0.205407	-0.066710	-0.124488	-0.237288	-0.449089	-0.198792	-0.230692	-0.100495	-0.520811	-0.308699	0.001603	-0.338568	0.146506	-0.083066	-0.160981	0.125811	0.812519	-0.425463	-0.497097	0.695280	-0.265159	-0.043956	0.224522	-0.483118	1.594094	5.865933	-3.389932	-0.532820	-0.956426	-0.847770	-0.008147	-0.316175	-0.057831	-0.181831	-0.370449	0.207443	0.024829	-0.708669	0.382464	0.101958	0.054491	-0.118635	-0.198574	-0.064656	-0.106369	-0.044582	-0.136223	-0.088453	0.118739	0.081802	0.058662	-0.120243	-0.114975	-0.147305	-0.032455	0.012907	0.010350	-0.070376	-0.125345	-0.100013	0.178665	-0.328442	-0.290382	0.004605	-0.098633	-0.106563	-0.174978	0.003670	-0.171749	-0.030454	-0.160686	-0.088235	-0.049885	-0.146354	0.020687	0.091310	-0.085712	0.191194	-0.036853	-0.227961	-0.158188	-0.116416	0.114118	-0.158092	-0.031987	0.164136	-0.064716	0.075683	-0.058872	0.031380	-0.085117	0.003234	-0.060703	-0.005989	-0.019529	0.132114	-0.138066	-0.043068	-0.048845	0.105723	0.006907	0.005168	0.096942	-0.353603	-0.180423	-0.201193	0.016554	0.043206	-0.001453	-0.022829	0.147152	-0.032314	0.070600	0.136919	-0.069818	-0.016255	0.201512	0.109765	0.006962	-0.058626	0.131766	-0.206312	-0.230839	-0.128875	1.233090	-0.524567	-0.069288	-0.039048	-0.014911	0.167241	0.271031	0.178742	-0.034539	0.449607	0.080966	-0.173549	0.044169	-0.035222	0.062597	-0.051253	-0.038977	-0.049965	-0.016291	-0.002645	-0.104362	-0.011617	0.002160	-0.072223	0.025964	-0.013008	0.002857	0.043406	0.007321	-0.005303	-0.000699	0.013769	0.000660	-0.016724	0.001320	0.000178	-0.000552	-0.028578	-0.005265	0.003328	0.004945	-0.008196	-0.000306	0.000200	0.010084	0.019227	0.010336	0.009096	0.043233	-0.011131	-0.024469	0.010955	0.035454	-0.022837	0.021715	-0.057549	0.025239	0.028902	-0.049782	-0.006342	-0.026603	-0.002942	-0.057084	0.022124	0.003381	-0.057972	-0.072170	-0.071880	0.039197	0.048447	0.150653	0.123678	0.009686	-0.013250	0.056986	-0.036353	-0.152211	0.047230	-0.059988	-0.054894	-0.083391	0.099022	-0.198829	0.035494	0.136022	0.047292	-0.076347	-0.002451	0.138738	0.030963	-0.029348	-0.375283	-0.241350	-0.297130	-0.205267	0.262311	0.002931	-0.148200	0.416982	-0.435787	0.010200	0.164588	0.326081	-0.142780	-0.149021	0.071213	-0.087520	0.018125	-0.111020	0.044942	-0.024689	-0.180849	-0.174874	-0.224991	-0.183709	0.010069	-0.119549	-0.092680	0.007332	-0.108653	-0.246026	-0.051974	-0.093387	-0.005088	0.179644	-0.122082	0.094056	-0.111098	0.016984	-0.028037	0.027459	-0.128117	-0.126827	-0.004236	0.020453	0.117548	-0.493684	0.380802	-0.356318	-0.345527	-0.293632	0.021088	-0.226347	-0.131183	-0.227184	0.533804	0.600246	-0.011850	-0.359462	0.067918	0.426015	0.529943	-0.383941	0.102393	0.103441	-0.109117	-0.295097	-0.182595	0.147071	0.016338	-0.119312	0.058241	-0.150590	0.063461	0.064448	-0.166495	0.058106	-0.077925	-0.065756	0.021276	0.108737	0.030786	-0.007117	0.022572	-0.021833	-0.036456	-0.020514	0.008522	0.001721	0.007231	-0.038603	0.043413	-0.062598	0.049743	0.048378	0.043156	0.026021	-0.013640	0.015648	0.018627	-0.033522	0.023908	0.010241	0.000658	0.027045	0.006543	0.004898	0.000056	-0.001713	0.019970	-0.005259	-0.017651	0.001101	-0.000428	-0.001169	-0.001682	0.001803	8.253140e-13	3.036098e-12	-1.282231e-14	4.822722e-13	-5.724605e-13	-8.322810e-14	3.717329e-14	-4.263749e-14	-1.219619e-12	6.586211e-13	1.358917e-12	-4.520304e-13	-1.495935e-13	-3.053587e-13	6.970258e-13	-6.801523e-13	-7.709663e-13	-6.007023e-14	-8.788278e-14	3.634391e-13	3.427984e-13	1.478694e-14	4.376906e-13	-2.124123e-13	-1.441653e-13	2.924280e-13	-3.475302e-13	2.044947e-13	5.594822e-13	0.000646	0.001351	0.003261	-0.000449	0.000830	0.008345	-0.000068	0.003175	0.016376	-0.007787	0.003761	-0.005638	-0.011902	0.001815	-0.034904	0.048006	0.075591	0.090641	-0.006339	-0.043793	0.021340	-0.029072	0.044513	0.013373	-0.083284	-0.077810	0.052233	0.008271	0.021237	-0.042025	0.198054	0.113263	-0.058153	0.094471	0.228296	-0.176797	-0.122531	0.204857	-0.378788	-0.199944	-0.363128	-0.012757	0.139071	-0.075139	0.253585	-0.090229	-0.217825	0.218308	-0.267917	0.199734	0.094606	0.074341	0.103974	-0.187454	-0.243285	0.170159	0.468823	0.124462	-0.274712	0.274267	-0.545699	0.352986	-0.046200	0.191029	0.390720	0.107193	-0.183584	1.686666	4.013364	3.697415	2.309273	9.864420	1.593944	3.489672	-0.785781	0.995907	1.091577	1.967451	1.967451	1.967451	1.757292	1.828258	1.828258	1.828258	1.757292	1.716682	1.716682	1.716682	1.684202	1.454577	1.454577	1.454577	1.684202	0.696360	0.519988	0.519988	0.519988	0.696360	0.527106	0.174842	0.174842	0.174842	0.527106	1.865922	2.481351	2.111799	1.518005	2.491871	1.725503	1.839066	1.895887	1.584613	2.135338	1.633639	1.793436	1.591288	3.791628	0.426820	2.293836	2.530565	1.559053	1.090102	2.382660	0.410595	-0.855140	2.306451	2.021639	2.321210	3.178760	-0.968738	-0.770785	-1.064489	-0.543447	-0.318908	-0.074659	-0.140241	-0.318073
2	-0.549461	0.549461	-0.243320	-0.200086	2.173241	-0.157398	-0.043311	-0.159226	-0.320339	-0.221602	-1.083970	-0.007421	-0.007421	-0.007421	-0.739256	-0.007421	134.751623	-1.183518	-0.007421	-0.007421	-0.258813	-0.017085	-0.155351	-0.099347	-0.561520	-0.133703	0.008739	-0.211100	-0.385805	0.249944	-1.098549	0.653056	-0.692954	-0.125449	-2.555751	3.164321	-0.052203	1.098404	-0.196219	0.460858	-0.464040	2.615813	0.221279	1.488161	0.097318	1.035531	-0.640831	3.362325	-3.844559	1.125514	-1.521700	-0.723552	-0.176947	-0.775217	-0.578675	1.033989	-0.951941	0.369220	0.133379	0.098876	0.149616	-1.490444	0.194185	-0.329183	-0.214546	0.224300	-0.307572	-0.026110	-0.131600	0.223520	0.090770	-0.076821	-0.052729	-0.052196	-0.119112	0.116269	0.024458	-0.026558	-0.126343	-0.197976	0.023151	-0.260938	-0.042340	0.072105	-0.029533	0.036162	-0.180051	-0.064309	0.075104	-0.106197	-0.151694	-0.231420	-0.106367	-0.031178	-0.009255	-0.065675	-0.127407	-0.191857	0.027734	-0.283441	-0.068381	-0.126702	-0.129831	-0.111300	-0.417849	0.155636	0.311076	-0.300572	0.016992	0.274355	-0.040008	-0.238912	-0.166051	-0.254214	-0.041283	-0.098426	-0.131329	-0.326457	-0.156098	-0.011257	-0.017541	0.021224	0.127563	0.101303	-0.037059	-0.154341	-0.051848	0.262180	-0.169659	0.210952	0.187486	-0.827832	-0.006134	-0.083404	0.271356	-0.557108	-0.521545	0.000665	0.227176	-0.078251	0.183786	-0.100993	0.242239	0.222050	0.177046	-0.533937	-0.061600	0.304169	-0.280282	-0.089176	-0.057149	0.034380	-0.057564	-0.091304	0.277372	-1.440545	-0.086590	0.372596	-0.440936	0.043286	-0.142858	-0.788251	-1.560627	-1.358089	0.460762	-0.257737	0.111929	0.540291	-1.094658	-0.444038	-0.087696	-1.772570	0.605983	0.818940	-2.372275	0.689227	0.677397	-1.374102	-3.337659	-5.023191	0.844339	0.982759	0.601445	1.100664	1.813789	-1.714559	-0.090288	1.538776	1.998732	2.086108	-0.273292	-1.747413	1.990532	-0.363398	1.175752	3.586258	0.749065	3.728046	0.228683	0.934633	0.215514	-2.508128	-1.804746	4.427921	0.118978	0.055027	1.158042	-1.499516	3.079435	-0.775953	-1.433359	-0.246414	-3.017052	1.986929	1.096578	0.747984	2.275328	-5.606073	3.671009	1.489590	-1.241723	-3.749192	-2.134990	8.933014	6.567214	0.032593	-3.171463	-0.538306	-4.845864	-1.255298	-5.763620	4.106369	-7.231835	-3.165886	-3.668339	0.931353	-3.003468	-4.123856	-0.071417	-2.660184	-0.241473	2.695922	-0.391085	-0.309550	-0.217746	-0.244788	0.045692	-0.207735	-0.365621	0.315363	-1.203033	-0.468928	-0.408268	-0.420352	-0.330091	-0.207700	0.981062	0.319396	0.419477	-0.162150	-0.460295	-0.235526	0.110202	0.727480	0.080552	-0.661481	-0.787856	-0.150934	-0.035326	0.083032	0.177802	-0.142434	0.170785	0.038726	0.176184	-0.057977	0.195241	0.240600	0.194533	-0.047749	0.013702	-0.099494	0.043197	0.008421	0.071515	-0.261065	0.164758	-0.204567	0.111747	0.224852	0.095345	-0.165600	-0.098104	0.185224	-1.007427	0.383425	-0.844146	0.595892	0.682144	0.536070	-0.048185	0.385394	0.886149	-0.180272	0.679478	-0.272291	-0.061443	0.372345	-0.357528	0.065238	-0.265427	-0.110998	-0.033170	0.184583	-0.193668	0.258645	-0.214488	-0.135857	0.304798	-0.146043	-0.552069	0.131506	0.037543	0.080747	0.025523	0.064832	-0.037481	0.065358	-0.192135	-0.066773	0.075565	-0.007293	0.009434	-0.045009	0.019042	0.000184	-0.124571	0.022683	-0.001122	0.007883	0.041079	0.061792	0.043677	0.007882	-0.082375	0.037232	0.003298	0.007174	0.061770	0.077339	-0.079842	-0.007876	-0.000433	0.000739	-0.005631	0.004462	-0.009006	-0.000520	0.000470	-0.001306	-0.000481	-0.000103	6.595787e-14	5.572357e-13	1.493610e-13	1.861709e-13	-1.458207e-13	1.938398e-13	4.905571e-13	-3.348726e-13	-4.624119e-13	4.307896e-13	7.588101e-13	-1.736184e-13	5.680962e-13	-2.892674e-13	2.425362e-13	2.819889e-13	-6.391905e-13	-2.246249e-13	-2.430504e-13	-1.775977e-13	7.003689e-14	3.942614e-13	-1.315704e-13	-4.112645e-13	4.396000e-13	4.707710e-13	-1.988568e-13	7.566560e-14	-1.218159e-15	0.000304	0.000227	0.001718	0.003683	-0.006668	0.013104	0.004652	0.007286	0.011800	-0.000609	-0.000471	0.009785	-0.008856	-0.051621	0.013161	0.068941	0.047872	0.036074	-0.053992	-0.160231	0.041248	-0.108446	-0.006411	0.048080	-0.053402	-0.053870	-0.204285	0.153997	0.013815	-0.113353	-0.039425	-0.145924	-0.333918	0.043463	-0.278368	-0.310839	-0.138256	-0.042843	0.353198	-0.252925	-0.081981	-0.385320	-0.021495	0.280847	0.318372	-0.507378	0.214363	-0.423207	-0.486062	-0.073222	-0.583009	-0.064513	-0.660985	0.708667	-0.263698	0.221439	0.245657	-0.499414	-0.009785	0.005127	0.424804	-0.511147	-0.067987	-0.069496	-0.111682	-0.021870	0.185835	0.187878	3.724114	3.534396	1.997752	9.864420	3.108090	3.489672	-0.597693	-0.891177	-1.024769	1.620283	1.620283	1.620283	1.757292	1.779978	1.779978	1.779978	1.757292	1.764859	1.764859	1.764859	1.684202	1.454577	1.454577	1.454577	1.684202	0.696360	0.470696	0.470696	0.470696	0.696360	0.477217	-0.114632	-0.114632	-0.114632	0.477217	1.593650	2.122842	0.558238	1.722099	2.322322	2.148563	2.219609	2.525239	1.649855	2.195268	1.968594	1.725175	2.200811	3.569658	1.417214	1.423639	-0.345984	1.118643	-1.299069	1.811528	0.007543	-0.516916	1.999208	2.375083	2.066492	3.091316	-1.018980	-1.093911	-0.594753	-0.430420	-0.437206	-0.074659	-0.081536	-0.318073
3	-0.549461	0.549461	-0.243320	-0.200086	-0.460142	-0.157398	-0.043311	-0.159226	-0.320339	-0.221602	0.922535	-0.007421	-0.007421	-0.007421	1.352711	-0.007421	-0.007421	-1.183518	-0.007421	-0.007421	-0.258813	-1.613048	2.113955	0.781513	-0.041090	0.284282	-0.038338	-0.676021	-1.427084	-0.080922	3.231922	1.295412	0.012508	0.332316	0.626892	0.607942	0.036492	0.229525	0.042520	0.390681	0.062942	-0.044912	0.096292	0.095894	-0.017115	0.124980	-0.027133	0.025587	-0.036045	0.104323	0.062240	-0.069196	0.049094	-0.004196	0.075716	0.044748	-0.003177	-0.075337	-0.000055	0.105245	-0.048945	-0.061112	-0.038348	-0.031764	-0.054545	-0.057354	-0.121143	0.013155	-0.007405	-0.030269	0.047155	-0.016913	0.092906	-0.039380	-0.077470	-0.082177	-0.037030	0.173840	-0.171706	-0.016847	0.016145	-0.062745	-0.022843	-0.015480	0.027843	-0.041203	0.003861	-0.008904	-0.046435	-0.061257	-0.046170	-0.038983	-0.051279	-0.072083	0.056905	-0.002039	0.001940	0.039230	0.003453	0.304404	1.413800	-0.526698	-0.375402	0.109597	-0.423688	-0.035690	-0.089234	-0.456109	0.205518	-0.009879	-0.199800	-0.843242	-1.662875	-1.626516	0.353621	-3.060685	-1.887535	-3.938735	-0.084146	0.777366	-1.160367	-2.086786	-3.793884	2.372960	17.346957	0.323497	7.185653	1.716609	0.493613	-7.657147	4.487332	5.395448	-2.494822	-2.274507	0.867822	0.654820	0.814867	-0.011625	-0.329482	0.196535	0.304050	-0.063660	1.023886	0.354228	-0.874284	0.076024	-0.260614	-0.520658	-0.654328	-0.061348	0.259709	0.510558	0.742844	-0.396823	-0.282553	0.314588	0.097260	0.054746	0.129444	0.039219	0.011476	0.229740	0.347145	0.139064	-0.038718	0.089252	-0.076511	0.001235	0.034633	0.043739	-0.017059	-0.046728	-0.018559	0.000869	-0.025033	-0.050967	-0.008663	0.032284	0.000171	0.031409	0.016561	0.008224	0.010151	-0.008566	0.015066	-0.008863	0.066714	-0.040589	-0.009727	-0.005400	-0.036307	-0.000109	-0.020808	-0.018713	-0.001896	0.014031	0.010852	-0.018255	0.048484	0.018804	0.008596	0.004784	0.020417	0.015194	-0.033876	-0.016839	-0.025777	-0.005566	-0.041549	-0.031028	-0.016025	-0.038980	-0.030797	0.020078	0.012062	0.040959	-0.008658	0.011459	0.007490	0.026479	-0.189486	-0.210200	0.011898	-0.014409	-0.101808	0.075313	0.206237	-0.077441	0.201667	0.069879	0.079310	0.142394	-0.192540	-0.021895	-0.111482	-0.326456	-0.042605	-0.208090	0.107562	0.066910	-0.273328	0.050957	0.069328	0.074669	0.395907	0.004283	0.162800	-0.221290	-0.041560	0.189613	0.257747	-0.024757	0.250510	-0.319735	0.076783	-0.275395	-0.132746	0.117053	0.066243	-0.116763	-0.027526	-0.214193	0.040811	0.055356	-0.026428	0.032285	-0.048165	-0.038001	-0.126100	0.107319	-0.030829	0.126672	0.056959	-0.029922	0.036021	-0.045177	-0.014616	-0.149680	0.040414	-0.034874	0.059327	-0.114990	0.084580	0.004520	-0.010172	0.184346	0.256402	-0.080829	0.032665	0.135759	0.021214	0.000104	0.102261	0.143784	0.051508	0.020533	-0.081731	0.155202	0.086739	0.155264	-0.093700	0.038151	0.104730	0.023600	0.023212	0.000399	0.017778	0.085027	-0.009863	0.095550	-0.040690	-0.044875	0.051347	0.019507	-0.064203	0.041495	-0.046404	-0.048103	0.013467	-0.002091	-0.051455	0.028990	0.022104	-0.008755	0.020917	0.001462	0.024211	-0.014315	-0.020055	-0.005578	0.023377	-0.003179	0.013755	-0.003950	0.009567	-0.010010	-0.003680	0.003423	0.004497	-0.001572	-0.001222	-0.004080	0.010599	-0.009886	-0.001997	-0.008885	0.016906	0.004692	0.004702	-0.000350	-0.013489	0.004569	0.001811	0.001931	-0.000296	0.007719	0.000198	-0.000149	0.000294	0.000246	-0.001287	-0.000022	5.403858e-13	1.871688e-12	-1.848559e-14	7.747310e-13	1.130374e-13	-3.566032e-13	-2.856897e-14	3.142888e-13	-2.752260e-13	3.778185e-13	5.790726e-13	2.552148e-13	3.743538e-14	1.696583e-13	-2.320552e-13	1.695277e-13	-5.041000e-13	6.517461e-13	6.850483e-14	3.872802e-13	1.045042e-13	4.540865e-13	3.039817e-13	-5.029946e-13	2.280004e-13	1.057661e-12	-1.686482e-14	4.138114e-13	4.350187e-13	0.000898	0.000064	-0.000592	0.000722	0.002130	-0.002865	-0.007820	0.002377	0.002385	-0.004110	-0.000110	-0.015326	0.006797	-0.066191	-0.010088	-0.001763	0.003451	0.035864	-0.024105	-0.026619	-0.001218	-0.039864	0.041715	0.013126	-0.074898	-0.036987	0.048937	-0.004382	-0.156708	0.005329	0.001246	0.125010	0.064376	-0.006293	-0.047094	-0.097946	0.021965	-0.097069	0.010510	-0.028651	0.024781	0.071788	0.036134	-0.135334	-0.066450	-0.091750	0.070086	-0.055203	0.005229	0.006194	-0.154561	-0.169844	0.041748	-0.063338	-0.202698	0.071541	0.020087	-0.091235	-0.146323	-0.156195	0.107056	-0.112310	0.218827	0.102852	0.125162	-0.151908	0.075588	0.401990	3.579489	3.534396	-0.465097	7.326477	0.079797	-1.800331	-1.161957	1.750741	0.129602	-2.545731	-2.545731	-2.545731	-2.517663	-2.516977	-2.516977	-2.516977	-2.517663	-2.522831	-2.522831	-2.522831	-2.554472	-2.565223	-2.565223	-2.565223	-2.554472	-2.551047	-2.486788	-2.486788	-2.486788	-2.551047	-2.516127	-2.382176	-2.382176	-2.382176	-2.516127	-1.782517	-1.667116	-1.800873	-0.590969	-1.690356	-1.976272	-1.422733	-1.365303	-0.111691	-0.981022	-0.510075	-0.459193	-0.237281	3.125717	-1.483228	-1.419003	0.161642	-1.272151	-0.104484	-1.822948	-1.028876	-0.806823	-1.943748	1.031994	-0.544371	0.817758	-1.621878	-1.232393	-1.534226	4.147180	4.058124	4.288340	4.203960	4.304691
4	-0.549461	0.549461	4.109822	-0.200086	-0.460142	-0.157398	-0.043311	-0.159226	-0.320339	-0.221602	-1.083970	-0.007421	-0.007421	-0.007421	-0.739256	-0.007421	-0.007421	0.844939	-0.007421	-0.007421	-0.258813	-0.091062	-0.142138	-0.155704	-0.290690	0.018882	0.023538	0.088675	-0.259151	0.118454	-0.277223	0.113376	-0.030246	-0.012391	-0.296987	-0.305950	0.638791	-0.402836	0.095289	-0.677695	-0.323365	2.921820	-0.528324	-0.916167	0.784182	-2.881635	-4.046828	-1.403529	0.811813	-0.457896	-1.484107	0.336951	0.651645	-0.199662	-0.009815	-0.996969	1.291357	0.552010	-0.001381	0.759154	6.402136	0.014643	-4.568361	-0.669343	-0.688380	-0.916656	-0.460104	-0.289265	-1.162298	0.393361	0.214624	0.007462	0.046069	-0.162350	-0.066949	0.125466	0.056490	-0.144479	-0.154396	-0.238182	0.140208	-0.092793	0.090896	0.107455	-0.228375	-0.123853	-0.045649	-0.096664	0.029720	-0.013703	0.025540	0.009465	0.436134	-0.207360	0.023093	-0.133724	-0.282796	-0.306715	0.035326	-0.189899	-0.043495	-0.135987	0.025136	-0.120619	-0.229064	0.199400	0.396874	0.200408	-0.227465	0.042851	-0.035421	10.683493	2.498634	1.421645	-1.076819	-0.811425	0.223074	5.204287	-3.543576	2.357343	11.674996	0.966778	4.815652	-5.562447	-2.146573	6.493908	5.744070	2.263234	-4.018663	-3.833655	3.351597	1.992660	-4.387845	-1.353734	-1.154061	0.345595	0.172170	-1.123522	-2.063017	0.341783	-0.902381	1.248841	-1.009345	0.932496	0.776984	-0.960601	0.800480	-0.117982	0.075546	0.405736	0.643886	0.206377	0.284722	0.039798	-0.485481	0.322969	0.596088	-0.173772	0.133415	1.125293	-0.347591	0.702950	1.358908	-1.904851	0.079878	0.238077	0.186989	0.109801	-0.030554	-0.035435	-0.126706	0.032382	-0.081465	-0.148267	0.075632	-0.023674	-0.059222	-0.016287	0.092370	0.035201	-0.026172	0.028214	0.001353	0.041429	0.003150	0.006559	0.030333	-0.001168	-0.043496	-0.025036	-0.011054	0.002166	-0.053098	0.008663	-0.005129	0.035345	-0.014254	0.009067	0.044906	-0.047469	-0.021653	0.000397	0.043235	-0.007156	0.037646	0.010363	-0.014186	-0.039908	-0.047054	-0.052000	-0.045913	0.079196	-0.028701	0.007140	0.000791	0.023369	0.026717	0.047993	0.052964	0.110131	-0.125628	-0.037942	-0.108221	0.018023	0.124822	0.097621	0.147474	0.097956	-0.015887	-0.138383	-0.070688	0.060297	-0.094765	-0.372871	-0.104256	0.055191	-0.089231	0.032969	0.175892	0.080074	-0.149253	0.041967	-0.336697	-0.024249	0.127932	0.011851	0.072007	-0.212062	-0.208177	-0.339806	0.279147	0.051760	0.237822	0.077641	-0.006038	0.215633	-0.205767	-0.107031	-0.076876	-0.051448	-0.120190	-0.071160	-0.042202	-0.022066	0.015799	0.113661	-0.034110	0.122412	-0.004128	-0.104020	0.001683	-0.034192	-0.031496	0.007867	-0.047550	-0.056216	0.097000	-0.136182	0.017754	0.108677	0.106493	0.039514	-0.066111	0.057269	0.182240	0.054177	0.086463	0.053867	-0.017969	-0.020106	0.024826	0.116873	-0.211760	-0.111568	0.044271	-0.124540	0.123316	0.064723	0.190813	-0.132186	-0.133371	-0.377998	0.065018	0.027507	-0.624117	-0.023784	0.528061	0.033043	0.190279	-0.029297	0.039950	-0.169085	0.037868	0.319105	-0.066247	-0.140469	-0.144764	0.279565	-0.095480	-0.180349	-0.136198	0.067375	0.200282	0.035690	0.074034	0.032853	-0.002653	0.128821	-0.104578	-0.012650	0.026667	0.030600	0.026710	-0.012186	0.000604	-0.044312	0.047991	-0.008702	-0.000343	-0.037231	-0.045570	0.002996	0.022491	-0.013916	-0.018276	-0.004874	-0.000790	0.006506	-0.006051	-0.007285	0.006555	0.004848	-0.002896	-0.010982	0.020604	-0.006821	-0.006159	-0.001374	-0.000871	-0.000553	-0.001962	0.001128	8.134414e-13	3.343869e-12	3.403775e-13	1.247558e-12	-5.078834e-13	-7.881740e-13	2.150963e-13	3.019587e-13	-1.342819e-12	8.734203e-13	1.534630e-12	-1.445148e-13	1.707971e-14	-7.835449e-14	2.055861e-13	-5.659225e-13	-8.610126e-13	1.877626e-13	-1.848306e-13	5.592423e-13	4.748970e-14	4.573081e-13	4.096153e-13	-3.742568e-13	-1.916247e-13	9.886954e-13	-8.823566e-14	4.101260e-13	8.240556e-13	0.001114	0.001579	0.003688	-0.000778	0.000855	0.009859	-0.002172	0.006321	0.018739	-0.008307	-0.001476	-0.017744	0.068083	-0.003997	-0.035762	0.049756	0.022789	0.009348	-0.048196	-0.012751	0.049627	0.074143	0.044665	0.040591	-0.001128	-0.020650	0.091526	-0.162773	-0.043332	0.041396	-0.008670	0.013198	0.135069	0.188542	-0.064135	0.108226	0.198994	-0.105229	-0.055194	0.036052	-0.393483	0.024604	-0.077289	-0.061464	-0.079109	-0.056735	-0.124512	0.146562	0.023865	0.033096	-0.099578	-0.150772	0.081821	0.116195	0.095288	0.114041	0.259925	-0.158098	-0.201357	0.106939	0.300085	0.046780	-0.287300	-0.043393	0.042920	-0.013065	0.006991	0.401990	3.579489	3.371377	2.503515	7.326477	3.108090	2.167171	-0.785781	-0.136343	-0.768242	1.521092	1.521092	1.521092	1.661226	1.683417	1.683417	1.683417	1.661226	1.716682	1.716682	1.716682	1.684202	1.752340	1.752340	1.752340	1.684202	1.295881	1.308650	1.308650	1.308650	1.295881	1.125774	0.802035	0.802035	0.802035	1.125774	2.356010	1.866764	0.155463	2.266350	2.209288	1.619738	2.056519	2.296384	2.498007	1.955548	0.896737	0.769514	1.049490	3.236703	0.922017	2.061783	-0.176775	1.684884	0.771546	2.278818	1.159120	0.691027	1.896793	2.869906	1.939132	2.566649	1.040923	0.475556	0.250772	-0.091339	-0.200610	-0.680632	-0.375063	-0.206681

Data Boxplot

def get_boxpolt_info(data, size=[10,5], axis_rotation=0):
    plt.rcParams['figure.figsize'] = size
    ax = sns.boxplot(data=data)
    plt.title('Numerical Features Distribution')
    if axis_rotation:
        plt.xticks(rotation=axis_rotation)
    plt.show()

get_boxpolt_info(df_ordinal[num_col_names], size=(20, 5), axis_rotation=90)

get_boxpolt_info(df_ordinal[cat_col_names], size=(20, 5), axis_rotation=90)

df_ordinal.reset_index(inplace=True, drop=True)
df_dummies.reset_index(inplace=True, drop=True)

label.reset_index(inplace=True, drop=True)

df_ordinal_final = df_ordinal.join(label)
df_ordinal_final.to_csv('./data/processed/1_1_processed_ordinal_encoding.csv', index_label=False)

df_dummies_final = df_dummies.join(label)
df_dummies_final.to_csv('../data/processed/1_1_processed_onehot_encoding.csv', index_label=False)